Location
Paternoster Square, London

Hours
Full Time

Salary
Negotiable

About the Role
Are you an experienced Data Science leader with a passion for building and scaling AI/ML products? Join London Stock Exchange Group as a Data Science Manager and lead a high-performing team of Data Scientists and ML Engineers developing next-generation AI solutions for financial markets. You will drive innovation using Large Language Models (LLMs), Generative AI, Deep Learning, Transformers, Agentic AI, and advanced Machine Learning to deliver real-world business impact. This role combines technical leadership, people management, and strategic execution to deliver production-grade AI products that solve complex customer problems and create measurable business value at scale. You will partner closely with Product, Engineering, Research, and business teams to shape AI strategy, accelerate execution, and foster a culture of innovation, collaboration, and continuous improvement. If you are passionate about shaping the future of AI and leading cutting-edge initiatives, we would love to hear from you.

Experience
- Proven track record of building, leading, and developing high-performing teams of Data Scientists, ML Engineers, and AI practitioners
- Demonstrated success delivering large-scale AI initiatives from ideation to production with measurable business impact
- Extensive experience designing, building, and deploying production-grade AI and Machine Learning solutions at scale
- Deep expertise in LLMs, Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI, Deep Learning, Neural Networks, and Transformer architectures
- Hands-on experience with LLM evaluation, benchmark design, model validation, prompt engineering, guardrails, and AI quality assessment
- Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users
- Strong understanding of model observability, monitoring, evaluation frameworks, experimentation, reliability, and operational excellence
- Expertise in cloud-native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architectures
- Experience implementing MLOps and LLMOps practices including CI/CD, model lifecycle management, governance, and production operations at scale
- Ability to communicate complex technical concepts clearly to technical, business, and executive audiences
- Strong stakeholder leadership skills with proven ability to drive alignment across Product, Engineering, Research, and Business teams

About you
- Exceptional leadership, communication, and influencing skills with experience engaging senior leadership and executive collaborators
- Strong coaching, mentoring, performance management, and talent development capabilities
- Ability to lead multiple concurrent programs balancing priorities, resources, and customer expectations
- Passionate about fostering a culture of innovation, collaboration, ownership, and continuous learning
- Experience working in financial services, capital markets, research, analytics, or other data-intensive domains is desirable
- Knowledge of multi-agent systems, autonomous workflows, copilots, intelligent AI assistants, reinforcement learning, fine-tuning, synthetic data generation, model compression, and advanced optimization techniques is a plus
- Familiarity with Knowledge Graphs, vector databases, semantic search, retrieval systems, and Graph RAG architectures is advantageous
- Understanding of Responsible AI, model governance, AI safety, privacy, regulatory compliance, and risk management frameworks
- Experience with DevOps, CI/CD, Kubernetes, Infrastructure as Code, containerization, and distributed computing platforms is beneficial
- Contributions to the AI community through publications, patents, conference presentations, research, or open-source projects are valued

Qualifications
Bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, Artificial Intelligence, Machine Learning, or a related quantitative field. A Master's degree or PhD is preferred.